Under the backdrop of Bitcoin mining reward halving, fluctuating energy prices, and competitive pressures, the global mining industry is facing a restructuring. Seeking business diversification and sustainable revenue models has become a common challenge for leading companies in the industry. Among these, transforming existing computing power infrastructure into artificial intelligence (AI) computing services has emerged as a noteworthy direction for transformation. Recently, the listed mining company CANG (CANG.US) clearly outlined its AI infrastructure development roadmap in its shareholder letter.

From Mining Networks to AI Node Networks

The reusability of computing power resources is the core driving factor on a technical level. Whether it is the mining machines required for Bitcoin mining or the GPU clusters needed for AI training and inference, they are essentially large-scale parallel computing units, sharing commonalities in rack layout, heat management, and network operations. The operational experience accumulated by mining companies in concentrated computing centers provides an important foundation for managing AI computing facilities.

Moreover, the advantages of electricity resources constitute a competitive barrier that is difficult to replicate. AI computing is known as an "energy-hungry beast," and its development speed is similarly constrained by the stability and economic feasibility of energy supply. Large mining companies like CANG have deployed low-cost, diversified energy infrastructures globally and have resolved the complex issues of grid connection and load balancing. As mentioned in CANG's shareholder letter, there is a power gap in the AI era, and its global grid-connected infrastructure is key to capturing opportunities, allowing it to offer AI computing power at marginal energy costs lower than traditional data centers.

Empowering Long-Tail Miners to Co-Create AI Infrastructure

CANG's differential advantage comes not only from its own energy or computing power resources but also from its ability to access a global network of small and medium-sized mines. These mines are distributed in regions with vastly different energy prices and supply-demand structures, originally serving cryptocurrency mining, now being integrated into a distributed infrastructure system supporting AI computing under CANG's platform deployment. According to publicly available information, CANG has established a wholly-owned subsidiary focused on AI computing, EcoHash Technology, and appointed a Chief Technology Officer for AI, forming a dedicated team to advance technological execution.

Traditional AI infrastructure is mostly concentrated in ultra-large-scale cloud or data centers, with a very high barrier to entry. CANG's model, however, is the opposite: by utilizing lightweight, modular GPU solutions, small and medium-sized mining companies can participate in the AI computing power market at a low cost. For these mining companies, previously underutilized scattered energy can be converted into stable productivity supporting AI computing through intelligent scheduling.

For the overall industry, this not only expands the geographic coverage of AI computing power supply but also creates a decentralized infrastructure layer with higher energy utilization efficiency. CANG's role thus transforms from merely a provider of energy and computing power to an AI engine for global long-tail mining operations.

Short-Term Monetization Path and Long-Term Vision

CANG's AI transformation blueprint shows a clear strategic ladder. Its planning is divided into three phases: in the near term, the group will spearhead the market with modular, containerized GPU computing nodes. This "plug-and-play" solution can be rapidly deployed on the group's existing global infrastructure, aiming to meet the massive long-tail AI inference needs of small and medium enterprises.

In the mid-term, CANG plans to develop a proprietary software-defined orchestration platform that integrates dispersed physical computing nodes worldwide into a unified, flexible, enterprise-grade computing network. This step is critical for transforming into a platform operator, aimed at lowering the technical barriers for customers using distributed computing power.

Looking long-term, it aims to build a mature global AI infrastructure platform that not only mobilizes the idle energy within its mining ecosystem but also integrates a broader range of underutilized power resources, ultimately establishing a persistent revenue stream that crosses market cycles through platform services and computing protocols.

Capital ReallocationEmpowerPower Landscape

Financial operations during the transformation process, particularly actions to adjust Bitcoin holdings, often attract market attention. This needs to be interpreted from a strategic overall perspective. CANG sells part of its Bitcoin holdings to strengthen its balance sheet and reduce financial leverage, aiming to raise funds for AI computing infrastructure expansion. This is a rational financial resource reallocation, transforming some high-volatility assets into productive capital investments that can generate future cash flow. Similar strategies have been seen in CleanSpark and Marathon, reflecting the balancing art that transforming companies seek between "grasping the upward potential of cryptocurrency assets" and "investing in a certain future."

More recently, CANG's capital actions further highlight its determination for transformation. EWCL has completed a $10.5 million equity investment, and CANG's chairman signed an agreement with a wholly-owned entity for a total equity investment of $65 million, with the proceeds explicitly used to support its expansion in the field of artificial intelligence (AI) and computing infrastructure, while further strengthening balance sheet structure. This series of capital reallocation actions not only constitutes a substantive endorsement of the company's strategic direction but also demonstrates the management's long-term confidence in the AI infrastructure track.

Diversifying the business is also a financial necessity to hedge against cycle fluctuations. The high volatility of the cryptocurrency market makes the performance of mining companies fluctuate with coin prices, while AI computing services that can generate recurring cash flow help smooth performance curves, enhance the company's appeal in capital markets, and achieve more robust long-term development.

CANG's practice reveals another possibility for mining companies to transform into AI: searching for efficiency advantages at the boundary of energy and computing power integration. Through platform integration and distributed deployment, CANG reconfigures the electricity, land, and cooling assets within the global mining network, allowing them to continuously contribute value in the AI arena. This model is not only lower in cost but also possesses evolutionary potential — it can dynamically switch between cryptocurrency computing power, AI inference, and local computing tasks, maximizing resource utilization according to market and technological changes.

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